The present invention relates to prediction of respiratory motion from 3D thoracic images and more particularly to predicting deformation of the lungs and surrounding organs during respiration based on thoracic images for improved image reconstruction.
Medical imaging and non-invasive interventions often suffer from complications caused by respiratory motion, which is a source of artifacts in images and makes it difficult to correctly determine the shape, size, and position of organs or lesions, such as lung tumors. The respiratory motion is complex, as the lungs do not just compress and deform, but also slide along the thoracic cavity. This behavior is enabled by the pleura, which is filled with a serous fluid and does not change its volume during respiration, hence maintaining the lung and the thoracic cavity. The anatomical properties of the pleura allow nearly friction free sliding of the lungs and diaphragm along the thoracic cavity. The motion is caused by two major groups of muscles: the diaphragm and the intercostal muscles. The contraction of these muscles enlarges the thoracic cavity and indirectly leads to an increase in lung volume.
Respiratory motion is required in a multitude of applications, like image reconstruction or therapy delivery. Yet, because of the complexity in the respiratory motion, accurate estimation of 3D lung deformation is extremely challenging. Lung deformation is typically approximated by one- or multi-dimensional signals from devices such as spirometers, abdominal pressure belts, external markers, or imaging modalities. These surrogate signals partially reflect the complex nature of lung deformation during a respiratory cycle. For image acquisition, for instance, a 4D computed tomography (CT) data set is compounded by image segments sorted and combined either based on the amplitude or the phase-angle of a respiratory surrogate, where the signal is assumed to be periodic. However, difficulties arise when the breathing pattern changes, which results in a non-periodic surrogate signal and causes imaging artifacts due to the combination of different breathing states. In a second approach, images are acquired at a specific instance of the respiratory cycle by triggering the imaging modality according to the surrogate signal. This is referred to as gating and is commonly used for nuclear imaging, such as positron emission tomography (PET). In radiotherapy, gating is typically used only to apply the ionizing radiation during pre-defined respiratory states.
Both approaches have drawbacks, such as the increase in radiation dose to achieve oversampling, or the increase of treatment or imaging time. Furthermore, for imaging, interpolation due to the lack of information between respiratory phases can cause step artifacts. Therefore, a continuous approximation of respiratory motion is necessary.